“The demand for AM is primarily driven by the automotive, aerospace, and emerging electric vehicle sectors. While Tier‑1 suppliers and prominent tooling houses in key industrial clusters have started printing and using conformal‑cooled tooling through LPBF processes”, says Dr. Vishwas Puttige, CEO and Director, Amace Solutions, in conversation with Neha Basudkar Ghate.
How is metal additive manufacturing (AM) enhancing and complementing traditional tooling and die manufacturing processes, particularly in terms of design flexibility and lead times?
Metal additive manufacturing (AM), specifically through the Laser Powder Bed Fusion (L‑PBF) process, complements traditional tooling and die manufacturing by offering unmatched design flexibility and significantly reducing lead times. Unlike conventional machining, AM enables the creation of complex conformal cooling channels and internal lattice structures that cannot be produced through traditional subtractive manufacturing methods.
This leads to direct and indirect improvements in productivity through enhanced heat transfer, extended tool life, and reduced cycle times—often by as much as 20% to 30%. It also decreases the rejection rate of parts caused by blow holes in die casting applications.
Additionally, the process can be made more cost‑effective through hybrid printing, where a conformal‑cooled insert is directly 3D printed onto a conventionally manufactured baseplate. This approach minimizes printing volume while retaining the value delivered through AM process, making it more attractive. In some cases, we have observed that lead times for producing tooling inserts can be reduced substantially compared with printing complete tooling inserts. These advantages together enable faster market introductions, providing a strong competitive edge in fast‑moving industries.
How is the Indian tooling and mould industry progressing in adopting additive manufacturing technologies, and what are the major barriers to its widespread adoption?
The Indian tooling industry is currently at an early but promising stage of additive manufacturing (AM) adoption. The demand for AM is primarily driven by the automotive, aerospace, and emerging electric vehicle sectors. While Tier‑1 suppliers and prominent tooling houses in key industrial clusters have started printing and using conformal‑cooled tooling through LPBF processes, a majority of small and medium tool rooms remain fragmented and continue to rely heavily on conventional CNC and EDM methods.
Typical barriers to wider adoption include the end users’ limited willingness to accord value to the intangible benefits of conformal‑cooled inserts, the high upfront capital investment required for AM machines and related post‑processing equipment, and a scarcity of workforce skilled in AM‑specific metallurgy and processes. These factors collectively increase perceived risk and slow down acceptance.
To overcome these challenges, there is a strong need for coordinated investments in workforce development, the strengthening of local metallurgy ecosystems, and government‑supported tooling clusters that can help mitigate barriers and accelerate adoption across the industry.
With continuous advancements in additive manufacturing, how is the industry improving material performance for demanding, high‑stress tooling applications?
Advancements in processes such as customised thermal treatments and microstructure analysis have enabled significant improvements in material performance for high‑stress tooling applications. 3D‑printable tool steels like H13 and maraging steel (1.2709) exhibit unique microstructures and possess higher residual stresses. To meet the demanding thermal fatigue and wear‑resistance requirements of tooling applications, new materials specifically developed for 3D printing are now emerging.
It is advisable to implement customised heat treatment cycles, such as annealing and ageing tailored to the characteristics of AM parts, rather than relying on conventional heat treatment regimes. Additional surface treatments, including shot blasting and specialised coatings, further enhance tool life.
There is clear evidence that, with proper process and post‑process control, AM tooling inserts can achieve comparable or even superior service lives, delivering performance gains of around 10% to 20% in shot counts under production conditions.
What strategies do you employ to mitigate the risks of residual stresses and distortions inherent in metal additive manufacturing, particularly for complex die components?
Preventing residual stress and distortion in metal additive manufacturing (AM) requires a disciplined, multi-stage approach. Before commencing 3D printing, we carefully set the part orientation, preheat the baseplate to reduce thermal gradients, program optimized scan vectors, and design supports that both restrain parts and conduct heat away from the printed component.
During the printing process, we maintain controlled parameters and employ in-situ monitoring systems, such as melt-pool monitoring and thermal/optical cameras, to detect anomalies early and make real-time corrections or pause printing to eliminate distorted parts.
After printing, the post-processing phase involves applying the right heat-treatment sequence to relieve tensile stresses and close sub-surface defects, followed by surface finishing and thorough quality inspection. Integrating these steps into a documented workflow with clear acceptance criteria for dimensional accuracy, surface finish, and repeatability significantly reduces rework costs and improves total lead times for complex die components.
How do you envision the future convergence of AI-driven design for additive manufacturing (DfAM) and generative manufacturing techniques impacting competitive advantage in tooling?
AI algorithms accelerate design iteration cycles by optimizing topologies, generating conformal cooling channels, and automating support placement, all while maintaining mechanical and thermal performance constraints. This convergence of AI-driven design and manufacturing techniques is set to profoundly shift tooling competitiveness in the future.
By integrating machine learning concepts with process simulation, manufacturers can predict part behavior prior to 3D printing, minimizing costly trial-and-error. Beyond enabling higher customisation, this integration shortens time-to-market and reduces overall process costs and material wastage. Organisations that embrace this transition are poised to lead in innovation, cost efficiency, and speed of delivery. However, this can only be realised when they invest in workforce up-skilling and the necessary software infrastructure.
In view of increasing material and energy costs, what innovations in additive manufacturing process efficiency and sustainability practices do you prioritise at the strategic level?
With the rise in raw material and energy costs, our strategic priorities in additive manufacturing are clear. To reduce energy consumption per part, we optimise build parameters and leverage data analytics. Given India’s constraints in the AM powder supply chain, we minimise waste and maximise value from each batch by implementing closed-loop powder reuse.
We can also cut emissions and costs by deploying gas recycling systems that recover and reuse nitrogen gas. Finally, we drive Artificial Intelligence (AI) and automation in process control, quality monitoring, and part tracking to enhance Overall Equipment Effectiveness (OEE). These practices not only safeguard our margins but also ensure we lead on sustainability, compliance, and global competitiveness for our customers.
This interview was published in TAGMA Times
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